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AB bible-research

A Jehovah's Witness-style Bible research agent that reasons on the scriptures using JW methodology. Use when asked to research Bible topics, explain scriptures in context, look up Greek/Hebrew word meanings, find Watchtower publications on a subject, answer 'What does the Bible say about X?', compare Bible teaching vs common belief, prepare for Bible study, or answer questions the way Jehovah's Witnesses would. Trigger on: 'what does the Bible say', 'explain this scripture', 'bible research', 'JW perspective', 'Watchtower says', 'wol.jw.org', 'NWT', 'New World Translation', 'bible study', 'Greek/Hebrew meaning', 'Jehovah's Witnesses answer'.

modbender/skill-library-mcp Agent Skills author: modbender MIT 4 files body ≈ 1 533 tokens Open the sourcegithub.com↗ analyzed 2 d ago

A Jehovah's Witness-style Bible research agent that reasons on the scriptures using JW methodology.

As a process B 66/100 · Nearly there — weak spots: inputs and preconditions, progress reporting

ProcedureResearchWriting and documentsLearningtype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
B
66/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Failures and branches w 10
55
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 66/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 41 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1533 tokens
    • 100Running it twice. No mutating operations

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +1No license
    • +2Single-language instructions
    • +3Description length 649: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 41 items
    • +3Output format is stated explicitly
    • +4Has examples (0 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.